This paper develops a decision support tool that can help ?shery authorities to forecast bivalve landings for the dredge ?eet accounting for several contextual conditions. These include weather conditions, phytotoxins epi- sodes, stock-biomass indicators per species and tourism levels. Vessel characteristics and ?shing e?ort are also taken into account for the estimation of landings. The relationship between these factors and monthly quantities landed per vessel is explored using multiple linear regression models and data mining techniques (random forests, support vector machines and neural networks). The models are speci?ed for di?erent regions in the Portugal mainland (Northwest, Southwest and South) using six years of data 2010–2015). Results showed that the impact of the contextual factors varies between regions and also depends on the vessels target species. The data mining techniques, namely the random forests, proved to be a robust decision support tool in this context, outperforming the predictive performance of the most popular technique used in this context, i.e. linear regression.

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Data mining,Random forests,Multiple regression,Forecasting,Small scale fisheries,Bivalve fisheries

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